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Multimethod, multistate Bayesian hierarchical modeling approach for use in regional monitoring of wolves

机译：用于狼的区域监视的多方法，多状态贝叶斯分层建模方法

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In many cases, the first step in large-carnivore management is to obtain objective, reliable, and cost-effective estimates of population parameters through procedures that are reproducible over time. However, monitoring predators over large areas is difficult, and the data have a high level of uncertainty. We devised a practical multimethod and multistate modeling approach based on Bayesian hierarchical-site-occupancy models that combined multiple survey methods to estimate different population states for use in monitoring large predators at a regional scale. We used wolves (Canis lupus) as our model species and generated reliable estimates of the number of sites with wolf reproduction (presence of pups). We used 2 wolf data sets from Spain (Western Galicia in 2013 and Asturias in 2004) to test the approach. Based on howling surveys, the naive estimation (i.e., estimate based only on observations) of the number of sites with reproduction was 9 and 25 sites in Western Galicia and Asturias, respectively. Our model showed 33.4 (SD 9.6) and 34.4 (3.9) sites with wolf reproduction, respectively. The number of occupied sites with wolf reproduction was 0.67 (SD 0.19) and 0.76 (0.11), respectively. This approach can be used to design more cost-effective monitoring programs (i.e., to define the sampling effort needed per site). Our approach should inspire well-coordinated surveys across multiple administrative borders and populations and lead to improved decision making for management of large carnivores on a landscape level. The use of this Bayesian framework provides a simple way to visualize the degree of uncertainty around population-parameter estimates and thus provides managers and stakeholders an intuitive approach to interpreting monitoring results. Our approach can be widely applied to large spatial scales in wildlife monitoring where detection probabilities differ between population states and where several methods are being used to estimate different population parameters.
机译：在许多情况下，大型食肉动物管理的第一步是通过随着时间的推移可重复生成的程序来获得客观，可靠且具有成本效益的种群参数估计。但是，很难在大范围内监视掠食者，并且数据具有高度的不确定性。我们基于贝叶斯分层站点占用模型设计了一种实用的多方法和多状态建模方法，该方法结合了多种调查方法来估计不同的种群状态，以用于在区域范围内监视大型捕食者。我们使用狼（Canis lupus）作为模型物种，并对狼繁殖（有幼崽）的地点数量产生了可靠的估计。我们使用了来自西班牙（2013年西加利西亚和2004年阿斯图里亚斯）的2个狼数据集来测试该方法。根据啸声调查，在西方加利西亚和阿斯图里亚斯，有繁殖场所的天真的估计（即仅基于观测的估计）分别为9和25。我们的模型分别显示了33.4（SD 9.6）和34.4（3.9）个具有狼繁殖能力的部位。狼繁殖的栖息地数量分别为0.67（SD 0.19）和0.76（0.11）。该方法可用于设计更具成本效益的监控程序（即定义每个站点所需的采样工作）。我们的方法应启发跨多个行政边界和人群进行协调良好的调查，并在景观层面上改善大型食肉动物管理的决策制定。这种贝叶斯框架的使用提供了一种简单的方法来可视化人口参数估计值周围的不确定性程度，从而为管理人员和利益相关者提供了一种直观的方法来解释监测结果。我们的方法可以广泛应用于野生动植物监测中的大型空间尺度，其中种群状态之间的检测概率不同，并且使用多种方法来估算不同的种群参数。

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